{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc25f309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          viewers_count   R-squared:                       0.896\n",
      "Model:                            OLS   Adj. R-squared:                  0.895\n",
      "Method:                 Least Squares   F-statistic:                     2248.\n",
      "Date:                Fri, 31 Oct 2025   Prob (F-statistic):          1.41e-130\n",
      "Time:                        20:20:07   Log-Likelihood:                -4920.6\n",
      "No. Observations:                 264   AIC:                             9845.\n",
      "Df Residuals:                     262   BIC:                             9852.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const            2.448e+07   3.89e+06      6.289      0.000    1.68e+07    3.21e+07\n",
      "streamers_count  9221.6550    194.484     47.416      0.000    8838.705    9604.605\n",
      "==============================================================================\n",
      "Omnibus:                        6.999   Durbin-Watson:                   0.260\n",
      "Prob(Omnibus):                  0.030   Jarque-Bera (JB):                6.861\n",
      "Skew:                           0.389   Prob(JB):                       0.0324\n",
      "Kurtosis:                       3.134   Cond. No.                     4.19e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.19e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 读取数据集\n",
    "data = pd.read_csv(\"./livestreaming.csv\")\n",
    "X = data[\"streamers_count\"]\n",
    "Y = data[\"viewers_count\"]\n",
    "\n",
    "# 构建一元回归模型\n",
    "X = sm.add_constant(X) # 添加截距项\n",
    "model= sm.OLS(Y,X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20d8cf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制一元线性回归拟合图\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams.update({\n",
    "    'font.size':8\n",
    "})\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(X['streamers_count'],Y,'c.') # X现在有两个Column， const（截距）和 streamers_count\n",
    "plt.plot(X['streamers_count'],model.predict(X),'salmon')\n",
    "plt.xlabel('主播数量')\n",
    "plt.ylabel('观众数量')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a18b657",
   "metadata": {},
   "source": [
    "## 基于多元回归的汽车燃油效率预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a0654ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 读取数据集\n",
    "data = pd.read_csv(\"/Volumes/orico/Programming/AUG/Python_Learn/数据挖掘/第四章/auto_mpg.csv\")\n",
    "data = data.dropna() # 缺失值处理\n",
    "corr_matrix = data.corr() # 计算相关系数矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5c44a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. 可视化相关系数矩阵\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "mask = np.triu(np.ones_like(corr_matrix, dtype=bool)) # 做一个上三角遮蔽矩阵\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.set_theme(font=\"Songti SC\" , font_scale=2)\n",
    "sns.heatmap(corr_matrix,annot=True,cmap=\"coolwarm\",mask=mask)\n",
    "plt.title('皮尔逊相关系数矩阵热力图',pad=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660fd4ab",
   "metadata": {},
   "source": [
    " 1. `sns.heatmap()`\n",
    "- **`sns`** 是 Seaborn 库的常用别名（通常通过 `import seaborn as sns` 导入）。\n",
    "- **`heatmap()`** 是 Seaborn 中用于绘制热力图的函数，特别适合展示二维数据（如相关性矩阵、混淆矩阵等）。\n",
    "\n",
    "---\n",
    "\n",
    " 1. 参数详解\n",
    "\n",
    "`corr_matrix`\n",
    "- 这是一个 **相关性矩阵**（correlation matrix），通常是通过 `pandas.DataFrame.corr()` 方法计算得到的。\n",
    "- 它是一个对称的二维 DataFrame 或 NumPy 数组，其中每个元素表示两个变量之间的相关系数（范围通常在 -1 到 1 之间）。\n",
    "  - **1**：完全正相关\n",
    "  - **0**：无线性相关\n",
    "  - **-1**：完全负相关\n",
    "\n",
    " `annot=True`\n",
    "- **`annot`** 是 \"annotation\"（注释）的缩写。\n",
    "- 当 `annot=True` 时，会在热力图的每个单元格中 **显示具体的数值**（即相关系数）。\n",
    "- 如果设为 `False`（默认），则只显示颜色，不显示数字。\n",
    "\n",
    " `cmap=\"coolwarm\"`\n",
    "- **`cmap`** 是 \"colormap\"（颜色映射）的缩写。\n",
    "- `\"coolwarm\"` 是一种 **发散型（diverging）颜色方案**：\n",
    "  - **蓝色（cool）** 表示负相关（值接近 -1）\n",
    "  - **白色** 表示无相关（值接近 0）\n",
    "  - **红色（warm）** 表示正相关（值接近 1）\n",
    "- 这种配色非常适合展示相关性，因为能直观区分正负关系。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c20049",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "由热力图可以看出， 燃气效率（mpg）与发动机排量、发动机气缸数，发动机功率， 汽车重量之间的相关性较强\n",
    "![](http://cdn.sealight.site/notes/202510301853762.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d7288b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "const",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "displacement",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "cylinders",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "horsepower",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "weight",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "acceleration",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "model_year",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "origin",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "20e0f54e-f068-46b0-a035-203be8460e84",
       "rows": [
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         "1",
         "1.0",
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       ],
       "shape": {
        "columns": 8,
        "rows": 5
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>displacement</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>8</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>8</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  displacement  cylinders  horsepower  weight  acceleration  \\\n",
       "0    1.0         307.0          8       130.0    3504          12.0   \n",
       "1    1.0         350.0          8       165.0    3693          11.5   \n",
       "2    1.0         318.0          8       150.0    3436          11.0   \n",
       "3    1.0         304.0          8       150.0    3433          12.0   \n",
       "4    1.0         302.0          8       140.0    3449          10.5   \n",
       "\n",
       "   model_year  origin  \n",
       "0          70       1  \n",
       "1          70       1  \n",
       "2          70       1  \n",
       "3          70       1  \n",
       "4          70       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[[\"displacement\",\"cylinders\",\"horsepower\",\"weight\",\"acceleration\",\"model_year\",\"origin\"]]\n",
    "y = data[[\"mpg\"]]\n",
    "# 构建模型\n",
    "X = sm.add_constant(X) # 添加截距项\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ac3896b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.821\n",
      "Model:                            OLS   Adj. R-squared:                  0.818\n",
      "Method:                 Least Squares   F-statistic:                     252.4\n",
      "Date:                Fri, 31 Oct 2025   Prob (F-statistic):          2.04e-139\n",
      "Time:                        20:20:08   Log-Likelihood:                -1023.5\n",
      "No. Observations:                 392   AIC:                             2063.\n",
      "Df Residuals:                     384   BIC:                             2095.\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const          -17.2184      4.644     -3.707      0.000     -26.350      -8.087\n",
      "displacement     0.0199      0.008      2.647      0.008       0.005       0.035\n",
      "cylinders       -0.4934      0.323     -1.526      0.128      -1.129       0.142\n",
      "horsepower      -0.0170      0.014     -1.230      0.220      -0.044       0.010\n",
      "weight          -0.0065      0.001     -9.929      0.000      -0.008      -0.005\n",
      "acceleration     0.0806      0.099      0.815      0.415      -0.114       0.275\n",
      "model_year       0.7508      0.051     14.729      0.000       0.651       0.851\n",
      "origin           1.4261      0.278      5.127      0.000       0.879       1.973\n",
      "==============================================================================\n",
      "Omnibus:                       31.906   Durbin-Watson:                   1.309\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.100\n",
      "Skew:                           0.529   Prob(JB):                     2.95e-12\n",
      "Kurtosis:                       4.460   Cond. No.                     8.59e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 8.59e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y,X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8246c320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.820\n",
      "Model:                            OLS   Adj. R-squared:                  0.818\n",
      "Method:                 Least Squares   F-statistic:                     351.7\n",
      "Date:                Fri, 31 Oct 2025   Prob (F-statistic):          2.73e-141\n",
      "Time:                        20:20:08   Log-Likelihood:                -1025.1\n",
      "No. Observations:                 392   AIC:                             2062.\n",
      "Df Residuals:                     386   BIC:                             2086.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const          -16.6939      4.120     -4.051      0.000     -24.795      -8.592\n",
      "displacement     0.0114      0.006      2.054      0.041       0.000       0.022\n",
      "horsepower      -0.0219      0.011     -2.033      0.043      -0.043      -0.001\n",
      "weight          -0.0063      0.001    -11.124      0.000      -0.007      -0.005\n",
      "model_year       0.7484      0.051     14.707      0.000       0.648       0.848\n",
      "origin           1.3853      0.277      4.998      0.000       0.840       1.930\n",
      "==============================================================================\n",
      "Omnibus:                       34.200   Durbin-Watson:                   1.295\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               58.468\n",
      "Skew:                           0.552   Prob(JB):                     2.01e-13\n",
      "Kurtosis:                       4.536   Cond. No.                     7.61e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.61e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X = data[[\"displacement\",\"horsepower\",\"weight\",\"model_year\",\"origin\"]]\n",
    "y = data[[\"mpg\"]]\n",
    "# 构建模型\n",
    "X = sm.add_constant(X) # 添加截距项\n",
    "model = sm.OLS(y,X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e26991",
   "metadata": {},
   "source": [
    "# Lasso 回归\n",
    "\n",
    "- 对上面的汽车燃油效率数据进行 Lasso 回归分析\n",
    "\n",
    "- 使用 sklearn.linear_model 包中的 Lasso 类构建 LASSO 回归模型。 参数 alpha 表示 LASSO 回归中的正则化项系数 $\\lambda$\n",
    "\n",
    "本例子中的模型参数为 Lasso(alpha=0.1).  使用 lasso.intercept_ 和 lasso.coef_ 分别打印截距和系数。 最后进行预测得到 y_pred_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e7a39d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_scaled: (313, 7) y_train: (313,)\n"
     ]
    }
   ],
   "source": [
    "# 准备 X_train_scaled 与 y_train（在 Lasso 单元格之前运行）\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "feature_cols = [\"displacement\", \"cylinders\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\", \"origin\"]\n",
    "\n",
    "# 保证与 y 同步清洗\n",
    "df = data[[\"mpg\"] + feature_cols].dropna()\n",
    "X = df[feature_cols]\n",
    "y = df[\"mpg\"].to_numpy()  # 1D 向量，便于 sklearn 使用\n",
    "\n",
    "# 划分训练/测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "# 标准化特征（仅在训练集上拟合）\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(\"X_train_scaled:\", X_train_scaled.shape, \"y_train:\", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c147de6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO 模型系数： [-0.    -0.    -0.523 -4.587  0.     2.67   1.078]\n",
      "LASSO 模型截距 23.599\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "import matplotlib.pyplot as plt \n",
    "lasso = Lasso(alpha=0.1)\n",
    "lasso.fit(X_train_scaled,y_train)\n",
    "print(\"LASSO 模型系数：\", np.round(lasso.coef_,3))\n",
    "print(\"LASSO 模型截距\",round(lasso.intercept_,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9ca8232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO 模型均方根误差（RMSE）： 3.303\n",
      "LASSO 模型决定系数（R²）： 0.786\n"
     ]
    }
   ],
   "source": [
    "# 预测测试集结果\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lasso.predict(X_test_scaled)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred)\n",
    "rmse_lasso = np.sqrt(mse_lasso)\n",
    "print(\"LASSO 模型均方根误差（RMSE）：\", round(rmse_lasso,3))\n",
    "r2_lasso = lasso.score(X_test_scaled, y_test)\n",
    "print(\"LASSO 模型决定系数（R²）：\", round(r2_lasso,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9d9c9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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